On July 2, I sat down with Dr. David Lewis, the Chief Data Scientist of Brainspace, via Webex. Dave and I covered a variety of topics dealing with the current application of machine learning to eDiscovery, ways to evaluate eDiscovery technology solutions, and what is likely to develop in the future of eDiscovery technology offerings.
Dr. David Lewis has over 30 years of data science experience, specifically with machine-learning and text classification. Dave holds numerous patents and joined Brainspace Corporation, an AppGate Company, in 2016. He holds a B.A. in Mathematics and an M.S. and Ph.D. in Computer Science.
Integrating Supervised Learning into a Workflow
Dave explains the need to understand a client’s large costs, as well as what actions get repeated per year or per project, as a company evaluates appropriate technologies and workflow.
In-House Counsel Considerations: Evaluating eDiscovery Products
Dave discusses spending time understanding the data to ensure that tools can handle the data, while knowing that eDiscovery products are not about “a magic solution,” but rather driven by a “detailed understanding of the messiness of real-world data.”
eDiscovery Technologies: What’s Next?
Dave opines on new developments emerging in the eDiscovery space, including increased interest in cross-modal data and integration across multiple data formats.
In-House Counsel: Leveraging Text Classification to Problem Solve
Dave discusses the use of Brainspace’s cluster wheel as a cost saving measure to identify and eliminate documents that are objectively useless to an investigation.
Brainspace as a text analytics software company focused in eDiscovery, investigations, compliance and related areas.
If you have any questions, please feel free to email me at email@example.com. I would love to hear from you.
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